"""simple docstring"""



import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


SCREAMING_SNAKE_CASE__:Optional[int]									=			logging.getLogger(__name__)






class  snake_case__      (       snake_case_       ):


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase=None    ,		lowerCamelCase=None						):
        __a       =    self.layer[current_layer](lowerCamelCase    ,		lowerCamelCase    ,		head_mask[current_layer]						)

        __a       =    layer_outputs[0]

        return hidden_states






@add_start_docstrings(
    """The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.""",						snake_case_,						)
class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__(lowerCamelCase						)

        __a       =    BertEncoderWithPabee(lowerCamelCase						)

        self.init_weights()
        __a       =    0
        __a       =    0
        __a       =    0

        __a       =    0


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    threshold


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    patience


 def 						a__   ( self						):
        __a       =    0
        __a       =    0


 def 						a__   ( self						):
        __a       =    self.inference_layers_num / self.inference_instances_num
        __a       =    (
            F"*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="
            F" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"
        )
        print(lowerCamelCase						)






 @add_start_docstrings_to_model_forward(lowerCamelCase						)
 def 						a__   ( self    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=False    ,		):

        if input_ids is not None and inputs_embeds is not None:
               raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"						)
        elif input_ids is not None:
               __a       =    input_ids.size()
        elif inputs_embeds is not None:
               __a       =    inputs_embeds.size()[:-1]
        else:
               raise ValueError("You have to specify either input_ids or inputs_embeds"						)

        __a       =    input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
               __a       =    torch.ones(lowerCamelCase    ,		device=lowerCamelCase						)
        if token_type_ids is None:
               __a       =    torch.zeros(lowerCamelCase    ,		dtype=torch.long    ,		device=lowerCamelCase						)

        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
        # ourselves in which case we just need to make it broadcastable to all heads.
        __a       =    self.get_extended_attention_mask(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)

        # If a 2D ou 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
               __a ,   __a ,   __a       =    encoder_hidden_states.size()
               __a       =    (encoder_batch_size, encoder_sequence_length)
               if encoder_attention_mask is None:
                      __a       =    torch.ones(lowerCamelCase    ,		device=lowerCamelCase						)
               __a       =    self.invert_attention_mask(lowerCamelCase						)
        else:
               __a       =    None

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        __a       =    self.get_head_mask(lowerCamelCase    ,		self.config.num_hidden_layers						)

        __a       =    self.embeddings(
            input_ids=lowerCamelCase    ,		position_ids=lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		inputs_embeds=lowerCamelCase						)
        __a       =    embedding_output

        if self.training:
               __a       =    []
               for i in range(self.config.num_hidden_layers						):
                      __a       =    self.encoder.adaptive_forward(
                          lowerCamelCase    ,		current_layer=lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		head_mask=lowerCamelCase						)

                      __a       =    self.pooler(lowerCamelCase						)
                      __a       =    output_layers[i](output_dropout(lowerCamelCase						)						)
                      res.append(lowerCamelCase						)
        elif self.patience == 0:  # Use all layers for inference
               __a       =    self.encoder(
                   lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		head_mask=lowerCamelCase    ,		encoder_hidden_states=lowerCamelCase    ,		encoder_attention_mask=lowerCamelCase    ,		)
               __a       =    self.pooler(encoder_outputs[0]						)
               __a       =    [output_layers[self.config.num_hidden_layers - 1](lowerCamelCase						)]
        else:
               __a       =    0
               __a       =    None
               __a       =    0
               for i in range(self.config.num_hidden_layers						):
                      calculated_layer_num += 1
                      __a       =    self.encoder.adaptive_forward(
                          lowerCamelCase    ,		current_layer=lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		head_mask=lowerCamelCase						)

                      __a       =    self.pooler(lowerCamelCase						)
                      __a       =    output_layers[i](lowerCamelCase						)
                      if regression:
                             __a       =    logits.detach()
                             if patient_result is not None:
                                    __a       =    patient_result.detach()
                             if (patient_result is not None) and torch.abs(patient_result - labels						) < self.regression_threshold:
                                    patient_counter += 1
                             else:
                                    __a       =    0
                      else:
                             __a       =    logits.detach().argmax(dim=1						)
                             if patient_result is not None:
                                    __a       =    patient_result.detach().argmax(dim=1						)
                             if (patient_result is not None) and torch.all(labels.eq(lowerCamelCase						)						):
                                    patient_counter += 1
                             else:
                                    __a       =    0

                      __a       =    logits
                      if patient_counter == self.patience:
                             break
               __a       =    [patient_result]
               self.inference_layers_num += calculated_layer_num
               self.inference_instances_num += 1

        return res






@add_start_docstrings(
    """Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. """,						snake_case_,						)
class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__(lowerCamelCase						)
        __a       =    config.num_labels

        __a       =    BertModelWithPabee(lowerCamelCase						)
        __a       =    nn.Dropout(config.hidden_dropout_prob						)
        __a       =    nn.ModuleList(
            [nn.Linear(config.hidden_size    ,		self.config.num_labels						) for _ in range(config.num_hidden_layers						)]						)

        self.init_weights()






 @add_start_docstrings_to_model_forward(lowerCamelCase						)
 def 						a__   ( self    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		lowerCamelCase=None    ,		):
        __a       =    self.bert(
            input_ids=lowerCamelCase    ,		attention_mask=lowerCamelCase    ,		token_type_ids=lowerCamelCase    ,		position_ids=lowerCamelCase    ,		head_mask=lowerCamelCase    ,		inputs_embeds=lowerCamelCase    ,		output_dropout=self.dropout    ,		output_layers=self.classifiers    ,		regression=self.num_labels == 1    ,		)

        __a       =    (logits[-1],)

        if labels is not None:
               __a       =    None
               __a       =    0
               for ix, logits_item in enumerate(lowerCamelCase						):
                      if self.num_labels == 1:
                             #  We are doing regression
                             __a       =    MSELoss()
                             __a       =    loss_fct(logits_item.view(-1						)    ,		labels.view(-1						)						)
                      else:
                             __a       =    CrossEntropyLoss()
                             __a       =    loss_fct(logits_item.view(-1    ,		self.num_labels						)    ,		labels.view(-1						)						)
                      if total_loss is None:
                             __a       =    loss
                      else:
                             total_loss += loss * (ix + 1)
                      total_weights += ix + 1
               __a       =    (total_loss / total_weights,) + outputs

        return outputs

"""simple docstring"""



import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


SCREAMING_SNAKE_CASE__:List[str]									=			logging.getLogger(__name__)



def 	_lowerCamelCase(	):
       __a       =    argparse.ArgumentParser(
           description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."				)
       parser.add_argument(
           "--dataset_name"   ,    type=a   ,    default="wikitext"   ,    help="Name of the training. Explore datasets at: hf.co/datasets."   ,    )
       parser.add_argument(
           "--dataset_config"   ,    type=a   ,    default="wikitext-103-raw-v1"   ,    help="Configuration name of the dataset."				)
       parser.add_argument(
           "--tokenizer_name_or_path"   ,    type=a   ,    default="sayakpaul/unigram-tokenizer-wikitext"   ,    help="Tokenizer identifier. Can be a local filepath or a Hub identifier."   ,    )
       parser.add_argument(
           "--shard_size"   ,    type=a   ,    default=1_0_0_0   ,    help="Number of entries to go in a single shard."   ,    )
       parser.add_argument("--split"   ,    type=a   ,    default="train"   ,    choices=["train", "test", "validation"]				)
       parser.add_argument(
           "--limit"   ,    default=a   ,    type=a   ,    help="Limit the number of shards (used for debugging)."   ,    )
       parser.add_argument(
           "--max_length"   ,    type=a   ,    default=5_1_2   ,    help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
           " sequence length that is a multiple of 8."   ,    )
       parser.add_argument(
           "--output_dir"   ,    default="tf-tpu"   ,    type=a   ,    help="Output directory where the TFRecord shards will be saved. If the"
           " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
           " shards will be directly saved to a Google Cloud Storage bucket."   ,    )

       __a       =    parser.parse_args()
       return args



def 	_lowerCamelCase(	a				):
       def fn(a				):
              return tokenizer(examples["text"]				)

       return fn



def 	_lowerCamelCase(	a				):
       __a       =    []
       for i in range(len(tokenized_data["input_ids"]				)				):
              __a       =    {
                  "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]				)				),
                  "attention_mask": tf.train.Feature(
                      intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]				)				),
              }
              __a       =    tf.train.Features(feature=a				)
              __a       =    tf.train.Example(features=a				)
              __a       =    example.SerializeToString()
              records.append(a				)
       return records



def 	_lowerCamelCase(	a				):
       __a       =    datasets.load_dataset(args.dataset_name   ,    args.dataset_config   ,    split=args.split				)

       if args.limit is not None:
              __a       =    min(len(a				)   ,    args.limit				)
              __a       =    dataset.select(range(a				)				)
              print(F"Limiting the dataset to {args.limit} entries."				)

       __a       =    AutoTokenizer.from_pretrained(args.tokenizer_name_or_path				)

       # Handle output directory creation.
       # For serializing into a Google Cloud Storage Bucket, one needs to first
       # create a bucket.
       if "gs" not in args.output_dir:
              if not os.path.exists(args.output_dir				):
                     os.makedirs(args.output_dir				)
              __a       =    os.path.join(args.output_dir   ,    args.split				)
              if not os.path.exists(a				):
                     os.makedirs(a				)
       else:
              __a       =    os.path.join(args.output_dir   ,    args.split				)

       # Tokenize the whole dataset at once.
       __a       =    tokenize_function(a				)
       __a       =    dataset.map(a   ,    batched=a   ,    num_proc=4   ,    remove_columns=["text"]				)

       # We need to concatenate all our texts together, and then split the result
       # into chunks of a fixed size, which we will call block_size. To do this, we
       # will use the map method again, with the option batched=True. When we use batched=True,
       # the function we pass to map() will be passed multiple inputs at once, allowing us
       # to group them into more or fewer examples than we had in the input.
       # This allows us to create our new fixed-length samples. The advantage of this
       # method is that we don't lose a whole lot of content from the dataset compared to the
       # case where we simply tokenize with a pre-defined max_length.

       def group_texts(a				):
              # Concatenate all texts.
              __a       =    {k: sum(examples[k]   ,    []				) for k in examples.keys()}
              __a       =    len(concatenated_examples[list(examples.keys()				)[0]]				)
              # We drop the small remainder, though you could add padding instead if the model supports it
              # In this, as in all things, we advise you to follow your heart 🫀
              __a       =    (total_length // args.max_length) * args.max_length
              # Split by chunks of max_len.
              __a       =    {
                  k: [t[i : i + args.max_length] for i in range(0   ,    a   ,    args.max_length				)]
                  for k, t in concatenated_examples.items()
              }
              return result

       __a       =    dataset_tokenized.map(a   ,    batched=a   ,    batch_size=1_0_0_0   ,    num_proc=4				)

       __a       =    0
       __a       =    0
       for shard in range(0   ,    len(a				)   ,    args.shard_size				):
              __a       =    grouped_dataset[shard : shard + args.shard_size]
              __a       =    len(dataset_snapshot["input_ids"]				)
              __a       =    os.path.join(a   ,    F"dataset-{shard_count}-{records_containing}.tfrecord"				)
              __a       =    get_serialized_examples(a				)

              with tf.io.TFRecordWriter(a				) as out_file:
                     for i in range(len(a				)				):
                            __a       =    serialized_examples[i]
                            out_file.write(a				)
                     print("Wrote file {} containing {} records".format(a   ,    a				)				)

              shard_count += 1
              total_records += records_containing

       with open(F"split-{args.split}-records-count.txt"   ,    "w"				) as f:
              print(F"Total {args.split} records: {total_records}"   ,    file=a				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[Any]									=			parse_args()
   main(args)
